Biology subjects

Zeidler, R.

Publications and source records attributed to Zeidler, R..

4 recordsLinked to original sources

bioRxiv2024

The nuclear speckles protein SRRM2 is a new therapeutic target molecule on the surface of cancer cells

The membrane composition of extracellular vesicles (EVs) largely reflects that of the plasma membrane of the cell of origin. We therefore hypothesized that EVs are a source for the detection of hitherto unknown tumor-associated druggable target molecules. For this, we used EVs derived from cancer cell lines for an immunization of a rat. From this immunization, we obtained a monoclonal antibody specific for SRRM2, a protein involved in splicing a major component of nuclear speckles. Here, we used this antibody to demonstrate that SRRM2 is exposed at the surface of most cancer cell lines from various entities and, even more important, on cancer cells in vivo. Moreover, we demonstrate that SRRM2-specific CAR-T cells are killing SRRM2-positive cancer cells electively. Collectively, we identified SRRM2 as a promising new target molecule exposed on the cancer cell surface and show that our SRRM2-specific antibody can be used as a basis for the development of new targeted cancer therapies.

cancer biology↗

bioRxiv2023

Single-cell precision nanotechnology in vivo

Targeting nanoparticle therapeutics with cellular accuracy in whole organisms could open breakthrough opportunities in precision medicine. However, evaluating and fine-tuning the biodistribution of such systems in the whole organism at the cellular level remains a major obstacle. Here, we constructed targetable DNA origami, and analyzed biodistribution in transparent mice, in addition to studying tolerability, clearance kinetics, and immune response parameters. Untargeted DNA origami primarily accumulated in the spleen and the liver, while an immune cell-targeting variant successfully attached to immune cells throughout the body. A cancer cell-targeting mimetic co-localized on solid-tumor metastasis in the liver and the lung. These findings indicate that DNA origami can be directed in vivo, providing an important proof-of-concept and highlights the potential of high-resolution tissue-clearing imaging technologies in their development. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/550304v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@ce216borg.highwire.dtl.DTLVardef@f40bb4org.highwire.dtl.DTLVardef@2c6a66org.highwire.dtl.DTLVardef@1fd7fde_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThis study demonstrates the potential of DNA origami-based drug delivery systems as versatile tool for for targeted delivery, which could be used to treat a range of diseases with applications. C_LIO_LIThe immune compatibility, half-life, targeting efficiency, and the biodistribution evaluation of DNA origami indicate its potential for systemic drug delivery. C_LIO_LIOur approach enables the assessment of biodistribution of nanoparticles in the intact body with a sensitivity to the single-cell level, highlighting the high-resolution tissue clearing technoloies in revealing DNA origamis feasibility for drug targeting. C_LI

bioengineering↗

bioRxiv2020

Micro RNAs are minor constituents of extracellular vesicles and are hardly delivered to target cells

Mammalian cells release different types of vesicles, collectively termed extracellular vesicles (EVs). EVs contain cellular microRNAs (miRNAs) with an apparent potential to deliver their miRNA cargo to recipient cells to affect the stability of individual mRNAs and the cells transcriptome. The extent to which miRNAs are exported via the EV route and whether they contribute to cell-cell communication are controversial. To address these issues, we analyzed the capacity of EVs to deliver packaged miRNAs into target cells and to exert biological functions. We applied well-defined approaches to produce and characterize purified EVs with or without specific viral miRNAs. We found that only a small fraction of EVs carried miRNAs. EVs readily bound to different target cell types, but there was no EV-cell membrane fusion or delivery of cargo. Importantly, the functionality of cells exposed to miRNA-carrying EVs was not affected. These results suggest EV-borne miRNAs do not act as effectors and question their relevancy in paracrine cell-to-cell communication. AUTHOR SUMMARYThe majority of metazoan cells release vesicles of different types and origins, such as exosomes and microvesicles, now collectively termed extracellular vesicles (EVs). EVs have gained much attention because they contain microRNAs (miRNAs) and thus could regulate their specific mRNA targets in recipient or acceptor cells that take up EVs. Using a novel fusion assay with superior sensitivity and specificity, we revisited this claim but found no convincing evidence for an efficient functional uptake of EVs in many different cell lines and primary human blood cells. Even EVs engineered to fuse and deliver their miRNA cargo to recipient cells had no measurable effect on target mRNAs in very carefully controlled, quantitative experiments. Our negative results clearly indicate that EVs do not act as vehicles for miRNA-based cell-to-cell communication.

cell biology↗

bioRxiv2019

Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of tumor cells more than 100-fold by applying the vDISCO method to image single cancer cells in intact transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantifications in a model of spontaneous metastasis using human breast cancer cells allowed us to systematically analyze clinically relevant features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in whole mice. DeepMACT can thus considerably improve the discovery of effective therapeutic strategies for metastatic cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/541862v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@ffe1b9org.highwire.dtl.DTLVardef@13c55eborg.highwire.dtl.DTLVardef@2ccb6corg.highwire.dtl.DTLVardef@df55e9_HPS_FORMAT_FIGEXP M_FIG C_FIG Supplementary Movies and deep learning algorithms of DeepMACT are available at http://discotechnologies.org/DeepMACT/

cancer biology↗